Image analysis – Image enhancement or restoration
Reexamination Certificate
1998-09-04
2002-12-17
Rogers, Scott (Department: 2724)
Image analysis
Image enhancement or restoration
C382S260000, C382S262000, C382S264000, C382S275000, C348S241000, C348S607000
Reexamination Certificate
active
06496604
ABSTRACT:
This invention relates to digital images, generated either directly by a digital acquisition system or digitized after their acquisition.
More precisely, the invention relates to a process for processing a source sequence of digital images that have been damaged (i.e. that have high noise) in order to obtain an output sequence of corrected digital images.
In general, the noise that affects digital images results in a degradation of the contrast of these images as a function of the noise intensity and a loss of information in damaged areas of these images.
There are several types of noise, particularly noise due to movement or defocusing of the sensor making the images blurred (motion/focus blur), speckle noise (in the form of spots), or noise due to measurements (additive or multiplication noise).
Therefore, noise effects should be compensated in order to restore a sequence of images with an adequate quality.
There are several known techniques of correcting damaged image sequences, particularly such as inverse filtering or calculating the average of the image sequence.
The known inverse filtering technique consists of applying the inverse transfer function to the damaged image. This assumes that the noise type (or an approximation of the noise type) is known. This is not always the case. Furthermore, this known inverse filtering technique is not very efficient when the noise/signal ratio is high.
In general, the second known technique that consists of calculating the average of the image sequence is not capable of providing sufficient correction to the damaged images.
The known inverse filtering technique has the major disadvantage of being specific to a particular noise type and that it introduces errors when the image sequence contains moving objects. Consequently, it will generate additional information losses when it is used to correct a noise type other than the noise type for which it was designed.
Furthermore, in most of these known techniques, each image in the sequence is corrected in several successive passes, each pass corresponding to a correction filtering, the parameters of which may be modified by an operator (this is referred to as interactive filtering). The time necessary to execute these successive passes makes it impossible to correct the sequence of damaged digital images in real time.
The objective of the invention is to overcome these various disadvantages in the state of the art.
More precisely, one of the objectives of this invention is to provide a process for processing a source sequence of damaged digital images, of the type capable of providing an output sequence of corrected digital images that is independent of the noise type affecting the images in the source sequence.
Another objective of the invention is to provide this type of process capable of processing the source sequence in real time.
Another objective of the invention is to provide a process capable of restoring a source sequence of images representing a panorama (with or without moving objects), and the contrast of these images.
These various objectives, and other objectives that will become apparent later, are achieved according to the invention by means of a process for processing a source sequence of damaged digital images of the type capable of producing an output sequence of corrected digital images, each of the damaged or corrected digital images being described pixel by pixel, each of the said pixels being characterized by an amplitude level among a plurality of possible amplitude levels,
characterized by the fact that the said process includes the following main basic steps, for each damaged digital image in the said source sequence:
for all pixels in the damaged image, calculate a first parameter called the global parameter, estimating the correction to be made to each pixel in the damaged image as a function of the said set of pixels in the damaged image and the set of pixels in a reference image, the said reference image being a corrected image resulting from the correction of the image preceding the said damaged image in the said source sequence;
for each given pixel in the damaged image, calculate a second parameter called the local parameter, estimating the correction to be made to the said given pixel as a function of the given pixel and other pixels called neighboring pixels, located within a predetermined vicinity of the said given pixel;
for each given pixel in the damaged image, calculate a third parameter called the temporal parameter, estimating the correction to be made to the said given pixel as a function of the said given pixel and the pixel in the reference image with the same spatial coordinates as the said given pixel;
for each given pixel in the damaged image, calculate a correction factor by combining the said first, second and third estimating parameters associated with the said given pixel;
correct each given pixel in the damaged image using a predetermined correction strategy, in order to obtain the pixel corresponding to the corrected image in the output sequence as a function of the correction factor associated with the said given pixel, the given pixel in the damaged image and the pixel in the reference image having the same spatial coordinates as the said given pixel.
Thus, the general principle of the invention consists of processing each of the damaged images forming the source sequence. Each pixel in each damaged image is corrected as a function of a correction factor associated with it and which is determined by combining three distinct parameters (the global, local and temporal parameters respectively) estimating the correction to be made to the pixel.
Due to this combination of three parameters, each supplying distinct information about the correction to be made, the process according to the invention is not interactive and all that is necessary is a single processing pass for each damaged image. Furthermore, most calculations can take place in matrix form. Consequently, the process according to the invention is ideal for use in real time.
Furthermore, this combination of three parameters makes the process according to the invention practically independent of the noise type affecting the images in the source sequence. This means that only a limited number of assumptions are necessary about the noise type that affected the images in the source sequence. For example, it is possible to only consider assumptions about how noise is distributed, intermittently. In other words, the process according to the invention can be adapted and is suitable for all types of noise to be compensated.
The first parameter (the global parameter) is common to all pixels in the same damaged image, and provides information about the general quality of this damaged image, by comparison with the previous corrected image (or the reference image).
Beneficially, the said calculation of the first parameter P
1
for all pixels in the damaged image can be made using the formula
P
1
=K+f
E
(
H
1
,H
2
)
where: K is a predetermined offset value;
H
1
is a first histogram of the amplitude levels of pixels in the damaged image;
H
2
is a second histogram of amplitude levels of pixels in the reference image;
f
E
is a predetermined error function used to calculate a variation between two functions.
For example, the predetermined error function may be based on the least squares method. It may also be based on the differences of the variances of histograms for the damaged image and for the reference image.
The predetermined offset value, if it is not equal to zero, prevents the first parameter from being zero when the two histograms are identical (if f
E
(H
1
; H
2
)=0).
The second parameter (the local parameter) is specific to each pixel in the same damaged image. It provides information about whether there is a spatial discontinuity, by comparing with neighboring pixels in the same damaged image. Noise results in a fairly pronounced spatial discontinuity in the same image. Consequently, detection of this type of spatial discontinuity in a pixel suggests a fairly
Key Concept
Kinney & Lange , P.A.
Rogers Scott
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